class_docstr: Text Processor class implement NER, POS tag, lexical tree parsing.
  EnProcessor is designed by single instance mode.
docstr: Lemmatize function.
  This method uses ``nltk.WordNetLemmatier`` to lemmatize tokens.
  
  :param list token_and_pos: *(token, POS)*.
  :return: A lemma or a list of lemmas depends on your input.
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code: def get_lemmas(self, token_and_pos):
      r"""
      Lemmatize function.
      This method uses ``nltk.WordNetLemmatier`` to lemmatize tokens.
  
      :param list token_and_pos: *(token, POS)*.
      :return: A lemma or a list of lemmas depends on your input.
  
      """
      self.check_initialized()
  
      if not isinstance(token_and_pos, list):
          token_and_pos = [token_and_pos]
      if self.__lemmatize is None:
          self.__lemmatize = self.nlp.pipeline[5][1]
  
      if self.__word2vec is None:
          self.__word2vec = self.nlp.pipeline[0][1]
  
      if self.__pos_tag is None:
          self.__pos_tag = self.nlp.pipeline[1][1]
  
      if self.__attribute_ruler is None:
          self.__attribute_ruler = self.nlp.pipeline[4][1]
  
      tokens = [tp[0] for tp in token_and_pos]
      tokens = Doc(self.nlp.vocab, words=tokens)
      tokens = self.__lemmatize(
          self.__attribute_ruler(
              self.__pos_tag(self.__word2vec(tokens))
          )
      )
  
      return [token.lemma_ for token in tokens]
  
imports: import re
  import nltk
  import spacy
  import threading
  from functools import reduce
  from spacy.tokens import Doc
  from .nltk_res_load import *
  from ..settings import *
label_desc: Use image node_id 14 for calling the EnProcessor obj's underlying member method code with example usage: obj.get_lemmas(token_and_pos) and returns: unknown